






"""simple docstring"""





import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
  import torch

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		True
except ImportError:
  _SCREAMING_SNAKE_CASE   :							List[Any]          =		False


try:
  from torch.hub import _get_torch_home

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		_get_torch_home()
except ImportError:
  _SCREAMING_SNAKE_CASE   :							Any          =		os.path.expanduser(
      os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
  )

_SCREAMING_SNAKE_CASE   :							str          =		os.path.join(torch_cache_home, '''transformers''')

_SCREAMING_SNAKE_CASE   :							Any          =		'''https://cdn.huggingface.co'''
_SCREAMING_SNAKE_CASE   :							Tuple          =		'''https://s3.amazonaws.com/models.huggingface.co/bert'''
_SCREAMING_SNAKE_CASE   :							Any          =		'''/'''.join(str(Path(__file__).resolve()).split('''/''')[:-1])
_SCREAMING_SNAKE_CASE   :							int          =		os.path.join(PATH, '''config.yaml''')
_SCREAMING_SNAKE_CASE   :							Tuple          =		os.path.join(PATH, '''attributes.txt''')
_SCREAMING_SNAKE_CASE   :							Tuple          =		os.path.join(PATH, '''objects.txt''')
_SCREAMING_SNAKE_CASE   :							str          =		os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
_SCREAMING_SNAKE_CASE   :							List[str]          =		os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		'''pytorch_model.bin'''
_SCREAMING_SNAKE_CASE   :							Optional[int]          =		'''config.yaml'''







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]=OBJECTS						,							_lowerCamelCase			:						Tuple=ATTRIBUTES					) ->							Dict:
  lowerCamelCase_       	=		[]
  with open(_lowerCamelCase					) as f:
    for object in f.readlines():
      vg_classes.append(object.split(','					)[0].lower().strip()					)

  lowerCamelCase_       	=		[]
  with open(_lowerCamelCase					) as f:
    for object in f.readlines():
      vg_attrs.append(object.split(','					)[0].lower().strip()					)
  return vg_classes, vg_attrs







def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							Optional[int]:
  lowerCamelCase_       	=		OrderedDict()
  with open(_lowerCamelCase						,							'rb'					) as f:
    lowerCamelCase_       	=		pkl.load(_lowerCamelCase					)['model']
  for k in copy.deepcopy(list(ckp.keys()					)					):
    lowerCamelCase_       	=		ckp.pop(_lowerCamelCase					)
    if isinstance(_lowerCamelCase						,							np.ndarray					):
      lowerCamelCase_       	=		torch.tensor(_lowerCamelCase					)
    else:
      assert isinstance(_lowerCamelCase						,							torch.tensor					), type(_lowerCamelCase					)
    lowerCamelCase_       	=		v
  return r






class 	a		:
    SCREAMING_SNAKE_CASE		:	List[Any]		=					{}






    def __init__(			self	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				dict			,		__SCREAMING_SNAKE_CASE	:				str = "root"			,		__SCREAMING_SNAKE_CASE	:				str=0   )	->						Optional[Any]:
      lowerCamelCase_       	=		name
      lowerCamelCase_       	=		level
      lowerCamelCase_       	=		{}
      for k, v in dictionary.items():
        if v is None:
          raise ValueError()
        lowerCamelCase_       	=		copy.deepcopy(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		copy.deepcopy(__SCREAMING_SNAKE_CASE   )
        if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          lowerCamelCase_       	=		Config(__SCREAMING_SNAKE_CASE			,		name=__SCREAMING_SNAKE_CASE			,		level=level + 1   )
        lowerCamelCase_       	=		v
        setattr(self			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		d






    def __repr__(			self	:				List[Any]   )	->						str:
      return str(list((self._pointer.keys())   )   )






    def __setattr__(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		val
      lowerCamelCase_       	=		val
      lowerCamelCase_       	=		key.split('.'   )
      lowerCamelCase_       	=		len(__SCREAMING_SNAKE_CASE   ) - 1
      lowerCamelCase_       	=		self._pointer
      if len(__SCREAMING_SNAKE_CASE   ) > 1:
        for i, l in enumerate(__SCREAMING_SNAKE_CASE   ):
          if hasattr(self			,		__SCREAMING_SNAKE_CASE   ) and isinstance(getattr(self			,		__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   ):
            setattr(getattr(self			,		__SCREAMING_SNAKE_CASE   )			,		'.'.join(levels[i:]   )			,		__SCREAMING_SNAKE_CASE   )
          if l == last_level:
            lowerCamelCase_       	=		val
          else:
            lowerCamelCase_       	=		pointer[l]






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      return self._pointer






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]   )	->						List[str]:
      with open(F'''{file_name}'''			,		'w'   ) as stream:
        dump(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]   )	->						Tuple:
      with open(F'''{file_name}'''			,		'w'   ) as stream:
        json.dump(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    @staticmethod
    def   UpperCamelCase			(			__SCREAMING_SNAKE_CASE	:				Tuple   )	->						List[str]:
      with open(__SCREAMING_SNAKE_CASE   ) as stream:
        lowerCamelCase_       	=		load(__SCREAMING_SNAKE_CASE			,		Loader=__SCREAMING_SNAKE_CASE   )
      return data






    def __str__(			self	:				Dict   )	->						int:
      lowerCamelCase_       	=		'    '
      if self._name != "root":
        lowerCamelCase_       	=		F'''{t * (self._level-1)}{self._name}:\n'''
      else:
        lowerCamelCase_       	=		''
      lowerCamelCase_       	=		self._level
      for i, (k, v) in enumerate(self._pointer.items()   ):
        if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          r += F'''{t * (self._level)}{v}\n'''
          self._level += 1
        else:
          r += F'''{t * (self._level)}{k}: {v} ({type(__SCREAMING_SNAKE_CASE   ).__name__})\n'''
        lowerCamelCase_       	=		level
      return r[:-1]






    @classmethod
    def   UpperCamelCase			(			cls	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				str			,		**__SCREAMING_SNAKE_CASE	:				str   )	->						str:
      lowerCamelCase_	,      lowerCamelCase_       	=		cls.get_config_dict(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
      return cls(__SCREAMING_SNAKE_CASE   )






    @classmethod
    def   UpperCamelCase			(			cls	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				str			,		**__SCREAMING_SNAKE_CASE	:				str   )	->						Union[str, Any]:
      lowerCamelCase_       	=		kwargs.pop('cache_dir'			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		kwargs.pop('force_download'			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		kwargs.pop('resume_download'			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		kwargs.pop('proxies'			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		kwargs.pop('local_files_only'			,		__SCREAMING_SNAKE_CASE   )

      if os.path.isdir(__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		os.path.join(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      elif os.path.isfile(__SCREAMING_SNAKE_CASE   ) or is_remote_url(__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		pretrained_model_name_or_path
      else:
        lowerCamelCase_       	=		hf_bucket_url(__SCREAMING_SNAKE_CASE			,		filename=__SCREAMING_SNAKE_CASE			,		use_cdn=__SCREAMING_SNAKE_CASE   )

      try:
        # Load from URL or cache if already cached
        lowerCamelCase_       	=		cached_path(
            __SCREAMING_SNAKE_CASE			,		cache_dir=__SCREAMING_SNAKE_CASE			,		force_download=__SCREAMING_SNAKE_CASE			,		proxies=__SCREAMING_SNAKE_CASE			,		resume_download=__SCREAMING_SNAKE_CASE			,		local_files_only=__SCREAMING_SNAKE_CASE			,		)
        # Load config dict
        if resolved_config_file is None:
          raise EnvironmentError

        lowerCamelCase_       	=		Config.load_yaml(__SCREAMING_SNAKE_CASE   )

      except EnvironmentError:
        lowerCamelCase_       	=		'Can\'t load config for'
        raise EnvironmentError(__SCREAMING_SNAKE_CASE   )

      if resolved_config_file == config_file:
        print('loading configuration file from path'   )
      else:
        print('loading configuration file cache'   )

      return Config.load_yaml(__SCREAMING_SNAKE_CASE   ), kwargs







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]					) ->							Any:
  lowerCamelCase_       	=		torch.load('dump.pt'						,							map_location=in_tensor.device					)
  lowerCamelCase_       	=		in_tensor.numpy()
  lowerCamelCase_       	=		out_tensor.numpy()[0]
  print(na.shape						,							na[0, 0, :5]					)
  print(na.shape						,							na[0, 0, :5]					)
  assert np.allclose(_lowerCamelCase						,							_lowerCamelCase						,							rtol=0.01						,							atol=0.1					), (
      F'''{sum([1 for x in np.isclose(_lowerCamelCase						,							_lowerCamelCase						,							rtol=0.01						,							atol=0.1					).flatten() if x is False]					)/len(na.flatten()					)*100:.4f} %'''
      " element-wise mismatch"
  )
  raise Exception('tensors are all good'					)

  # Hugging face functions below







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]					) ->							Dict:
  lowerCamelCase_       	=		urlparse(_lowerCamelCase					)
  return parsed.scheme in ("http", "https")







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						str						,							_lowerCamelCase			:						Tuple=True					) ->							str:
  lowerCamelCase_       	=		CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  lowerCamelCase_       	=		'/' not in model_id
  if legacy_format:
    return F'''{endpoint}/{model_id}-{filename}'''
  else:
    return F'''{endpoint}/{model_id}/{filename}'''







def 	lowerCamelCase__			(				_lowerCamelCase			:						Union[str, Any]						,							_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						Union[str, Any]=None						,							_lowerCamelCase			:						Optional[Any]=0						,							_lowerCamelCase			:						List[str]=None						,							) ->							int:
  lowerCamelCase_       	=		'python/{}'.format(sys.version.split()[0]					)
  if _torch_available:
    ua += "; torch/{}".format(torch.__version__					)
  if isinstance(_lowerCamelCase						,							_lowerCamelCase					):
    ua += "; " + "; ".join('{}/{}'.format(_lowerCamelCase						,							_lowerCamelCase					) for k, v in user_agent.items()					)
  elif isinstance(_lowerCamelCase						,							_lowerCamelCase					):
    ua += "; " + user_agent
  lowerCamelCase_       	=		{'user-agent': ua}
  if resume_size > 0:
    lowerCamelCase_       	=		'bytes=%d-' % (resume_size,)
  lowerCamelCase_       	=		requests.get(_lowerCamelCase						,							stream=_lowerCamelCase						,							proxies=_lowerCamelCase						,							headers=_lowerCamelCase					)
  if response.status_code == 416:  # Range not satisfiable
    return
  lowerCamelCase_       	=		response.headers.get('Content-Length'					)
  lowerCamelCase_       	=		resume_size + int(_lowerCamelCase					) if content_length is not None else None
  lowerCamelCase_       	=		tqdm(
      unit='B'						,							unit_scale=_lowerCamelCase						,							total=_lowerCamelCase						,							initial=_lowerCamelCase						,							desc='Downloading'						,							)
  for chunk in response.iter_content(chunk_size=1024					):
    if chunk:  # filter out keep-alive new chunks
      progress.update(len(_lowerCamelCase					)					)
      temp_file.write(_lowerCamelCase					)
  progress.close()







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[int]						,							_lowerCamelCase			:						Optional[int]=None						,							_lowerCamelCase			:						Tuple=False						,							_lowerCamelCase			:						List[Any]=None						,							_lowerCamelCase			:						Optional[Any]=10						,							_lowerCamelCase			:						Optional[int]=False						,							_lowerCamelCase			:						str=None						,							_lowerCamelCase			:						Dict=False						,							) ->							int:
  if cache_dir is None:
    lowerCamelCase_       	=		TRANSFORMERS_CACHE
  if isinstance(_lowerCamelCase						,							_lowerCamelCase					):
    lowerCamelCase_       	=		str(_lowerCamelCase					)

  os.makedirs(_lowerCamelCase						,							exist_ok=_lowerCamelCase					)

  lowerCamelCase_       	=		None
  if not local_files_only:
    try:
      lowerCamelCase_       	=		requests.head(_lowerCamelCase						,							allow_redirects=_lowerCamelCase						,							proxies=_lowerCamelCase						,							timeout=_lowerCamelCase					)
      if response.status_code == 200:
        lowerCamelCase_       	=		response.headers.get('ETag'					)
    except (EnvironmentError, requests.exceptions.Timeout):
      # etag is already None
      pass

  lowerCamelCase_       	=		url_to_filename(_lowerCamelCase						,							_lowerCamelCase					)

  # get cache path to put the file
  lowerCamelCase_       	=		os.path.join(_lowerCamelCase						,							_lowerCamelCase					)

  # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
  # try to get the last downloaded one
  if etag is None:
    if os.path.exists(_lowerCamelCase					):
      return cache_path
    else:
      lowerCamelCase_       	=		[
          file
          for file in fnmatch.filter(os.listdir(_lowerCamelCase					)						,							filename + '.*'					)
          if not file.endswith('.json'					) and not file.endswith('.lock'					)
      ]
      if len(_lowerCamelCase					) > 0:
        return os.path.join(_lowerCamelCase						,							matching_files[-1]					)
      else:
        # If files cannot be found and local_files_only=True,
        # the models might've been found if local_files_only=False
        # Notify the user about that
        if local_files_only:
          raise ValueError(
              'Cannot find the requested files in the cached path and outgoing traffic has been'
              ' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
              ' to False.'					)
        return None

    # From now on, etag is not None.
  if os.path.exists(_lowerCamelCase					) and not force_download:
    return cache_path

  # Prevent parallel downloads of the same file with a lock.
  lowerCamelCase_       	=		cache_path + '.lock'
  with FileLock(_lowerCamelCase					):
    # If the download just completed while the lock was activated.
    if os.path.exists(_lowerCamelCase					) and not force_download:
      # Even if returning early like here, the lock will be released.
      return cache_path

    if resume_download:
      lowerCamelCase_       	=		cache_path + '.incomplete'

      @contextmanager
      def _resumable_file_manager():
        with open(_lowerCamelCase						,							'a+b'					) as f:
          yield f

      lowerCamelCase_       	=		_resumable_file_manager
      if os.path.exists(_lowerCamelCase					):
        lowerCamelCase_       	=		os.stat(_lowerCamelCase					).st_size
      else:
        lowerCamelCase_       	=		0
    else:
      lowerCamelCase_       	=		partial(tempfile.NamedTemporaryFile						,							dir=_lowerCamelCase						,							delete=_lowerCamelCase					)
      lowerCamelCase_       	=		0

    # Download to temporary file, then copy to cache dir once finished.
    # Otherwise you get corrupt cache entries if the download gets interrupted.
    with temp_file_manager() as temp_file:
      print(
          '%s not found in cache or force_download set to True, downloading to %s'						,							_lowerCamelCase						,							temp_file.name						,							)

      http_get(
          _lowerCamelCase						,							_lowerCamelCase						,							proxies=_lowerCamelCase						,							resume_size=_lowerCamelCase						,							user_agent=_lowerCamelCase						,							)

    os.replace(temp_file.name						,							_lowerCamelCase					)

    lowerCamelCase_       	=		{'url': url, 'etag': etag}
    lowerCamelCase_       	=		cache_path + '.json'
    with open(_lowerCamelCase						,							'w'					) as meta_file:
      json.dump(_lowerCamelCase						,							_lowerCamelCase					)

  return cache_path







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						Dict=None					) ->							Optional[int]:
  lowerCamelCase_       	=		url.encode('utf-8'					)
  lowerCamelCase_       	=		shaaaa(_lowerCamelCase					)
  lowerCamelCase_       	=		url_hash.hexdigest()

  if etag:
    lowerCamelCase_       	=		etag.encode('utf-8'					)
    lowerCamelCase_       	=		shaaaa(_lowerCamelCase					)
    filename += "." + etag_hash.hexdigest()

  if url.endswith('.h5'					):
    filename += ".h5"

  return filename







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						int=None						,							_lowerCamelCase			:						Dict=False						,							_lowerCamelCase			:						List[str]=None						,							_lowerCamelCase			:						Dict=False						,							_lowerCamelCase			:						Optional[int]=None						,							_lowerCamelCase			:						Optional[int]=False						,							_lowerCamelCase			:						List[Any]=False						,							_lowerCamelCase			:						Union[str, Any]=False						,							) ->							int:
  if cache_dir is None:
    lowerCamelCase_       	=		TRANSFORMERS_CACHE
  if isinstance(_lowerCamelCase						,							_lowerCamelCase					):
    lowerCamelCase_       	=		str(_lowerCamelCase					)
  if isinstance(_lowerCamelCase						,							_lowerCamelCase					):
    lowerCamelCase_       	=		str(_lowerCamelCase					)

  if is_remote_url(_lowerCamelCase					):
    # URL, so get it from the cache (downloading if necessary)
    lowerCamelCase_       	=		get_from_cache(
        _lowerCamelCase						,							cache_dir=_lowerCamelCase						,							force_download=_lowerCamelCase						,							proxies=_lowerCamelCase						,							resume_download=_lowerCamelCase						,							user_agent=_lowerCamelCase						,							local_files_only=_lowerCamelCase						,							)
  elif os.path.exists(_lowerCamelCase					):
    # File, and it exists.
    lowerCamelCase_       	=		url_or_filename
  elif urlparse(_lowerCamelCase					).scheme == "":
    # File, but it doesn't exist.
    raise EnvironmentError('file {} not found'.format(_lowerCamelCase					)					)
  else:
    # Something unknown
    raise ValueError('unable to parse {} as a URL or as a local path'.format(_lowerCamelCase					)					)

  if extract_compressed_file:
    if not is_zipfile(_lowerCamelCase					) and not tarfile.is_tarfile(_lowerCamelCase					):
      return output_path

    # Path where we extract compressed archives
    # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
    lowerCamelCase_	,      lowerCamelCase_       	=		os.path.split(_lowerCamelCase					)
    lowerCamelCase_       	=		output_file.replace('.'						,							'-'					) + '-extracted'
    lowerCamelCase_       	=		os.path.join(_lowerCamelCase						,							_lowerCamelCase					)

    if os.path.isdir(_lowerCamelCase					) and os.listdir(_lowerCamelCase					) and not force_extract:
      return output_path_extracted

    # Prevent parallel extractions
    lowerCamelCase_       	=		output_path + '.lock'
    with FileLock(_lowerCamelCase					):
      shutil.rmtree(_lowerCamelCase						,							ignore_errors=_lowerCamelCase					)
      os.makedirs(_lowerCamelCase					)
      if is_zipfile(_lowerCamelCase					):
        with ZipFile(_lowerCamelCase						,							'r'					) as zip_file:
          zip_file.extractall(_lowerCamelCase					)
          zip_file.close()
      elif tarfile.is_tarfile(_lowerCamelCase					):
        lowerCamelCase_       	=		tarfile.open(_lowerCamelCase					)
        tar_file.extractall(_lowerCamelCase					)
        tar_file.close()
      else:
        raise EnvironmentError('Archive format of {} could not be identified'.format(_lowerCamelCase					)					)

    return output_path_extracted

  return output_path







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						Dict=","					) ->							Union[str, Any]:
  assert isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  if os.path.isfile(_lowerCamelCase					):
    with open(_lowerCamelCase					) as f:
      lowerCamelCase_       	=		eval(f.read()					)
  else:
    lowerCamelCase_       	=		requests.get(_lowerCamelCase					)
    try:
      lowerCamelCase_       	=		requests.json()
    except Exception:
      lowerCamelCase_       	=		req.content.decode()
      assert data is not None, "could not connect"
      try:
        lowerCamelCase_       	=		eval(_lowerCamelCase					)
      except Exception:
        lowerCamelCase_       	=		data.split('\n'					)
    req.close()
  return data







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							Union[str, Any]:
  lowerCamelCase_       	=		requests.get(_lowerCamelCase					)
  lowerCamelCase_       	=		np.array(Image.open(BytesIO(response.content					)					)					)
  return img







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]					) ->							str:
  lowerCamelCase_       	=		url.split('/'					)[-1]
  if fn not in os.listdir(os.getcwd()					):
    wget.download(_lowerCamelCase					)
  with open(_lowerCamelCase						,							'rb'					) as stream:
    lowerCamelCase_       	=		pkl.load(_lowerCamelCase					)
  lowerCamelCase_       	=		weights.pop('model'					)
  lowerCamelCase_       	=		{}
  for k, v in model.items():
    lowerCamelCase_       	=		torch.from_numpy(_lowerCamelCase					)
    if "running_var" in k:
      lowerCamelCase_       	=		torch.tensor([0]					)
      lowerCamelCase_       	=		k.replace('running_var'						,							'num_batches_tracked'					)
      lowerCamelCase_       	=		zero
  return new







def 	lowerCamelCase__			(				) ->							str:
  print(F'''{os.path.abspath(os.path.join(_lowerCamelCase						,							os.pardir					)					)}/demo.ipynb'''					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						Tuple="RGB"					) ->							Tuple:
  assert isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  if os.path.isfile(_lowerCamelCase					):
    lowerCamelCase_       	=		cva.imread(_lowerCamelCase					)
  else:
    lowerCamelCase_       	=		get_image_from_url(_lowerCamelCase					)
    assert img is not None, F'''could not connect to: {im}'''
  lowerCamelCase_       	=		cva.cvtColor(_lowerCamelCase						,							cva.COLOR_BGR2RGB					)
  if input_format == "RGB":
    lowerCamelCase_       	=		img[:, :, ::-1]
  return img







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						Union[str, Any]=1					) ->							Dict:
  return (images[i : i + batch] for i in range(0						,							len(_lowerCamelCase					)						,							_lowerCamelCase					))












"""simple docstring"""





from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin






@dataclass
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	torch.FloatTensor






class 	a		(   __snake_case    , __snake_case						):






    @register_to_config
    def __init__(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				int = 32			,		__SCREAMING_SNAKE_CASE	:				int = 64			,		__SCREAMING_SNAKE_CASE	:				int = 20			,		__SCREAMING_SNAKE_CASE	:				int = 768			,		__SCREAMING_SNAKE_CASE	:				int=77			,		__SCREAMING_SNAKE_CASE	:				Any=4			,		__SCREAMING_SNAKE_CASE	:				float = 0.0			,		__SCREAMING_SNAKE_CASE	:				str = "silu"			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = "linear"			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = "prd"			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = None			,		)	->						List[Any]:
      super().__init__()
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		attention_head_dim
      lowerCamelCase_       	=		num_attention_heads * attention_head_dim
      lowerCamelCase_       	=		additional_embeddings

      lowerCamelCase_       	=		time_embed_dim or inner_dim
      lowerCamelCase_       	=		embedding_proj_dim or embedding_dim
      lowerCamelCase_       	=		clip_embed_dim or embedding_dim

      lowerCamelCase_       	=		Timesteps(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		0   )
      lowerCamelCase_       	=		TimestepEmbedding(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		out_dim=__SCREAMING_SNAKE_CASE			,		act_fn=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		nn.Linear(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      if embedding_proj_norm_type is None:
        lowerCamelCase_       	=		None
      elif embedding_proj_norm_type == "layer":
        lowerCamelCase_       	=		nn.LayerNorm(__SCREAMING_SNAKE_CASE   )
      else:
        raise ValueError(F'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''   )

      lowerCamelCase_       	=		nn.Linear(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      if encoder_hid_proj_type is None:
        lowerCamelCase_       	=		None
      elif encoder_hid_proj_type == "linear":
        lowerCamelCase_       	=		nn.Linear(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      else:
        raise ValueError(F'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''   )

      lowerCamelCase_       	=		nn.Parameter(torch.zeros(1			,		num_embeddings + additional_embeddings			,		__SCREAMING_SNAKE_CASE   )   )

      if added_emb_type == "prd":
        lowerCamelCase_       	=		nn.Parameter(torch.zeros(1			,		1			,		__SCREAMING_SNAKE_CASE   )   )
      elif added_emb_type is None:
        lowerCamelCase_       	=		None
      else:
        raise ValueError(
            F'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''   )

      lowerCamelCase_       	=		nn.ModuleList(
          [
              BasicTransformerBlock(
                  __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		dropout=__SCREAMING_SNAKE_CASE			,		activation_fn='gelu'			,		attention_bias=__SCREAMING_SNAKE_CASE			,		)
              for d in range(__SCREAMING_SNAKE_CASE   )
          ]   )

      if norm_in_type == "layer":
        lowerCamelCase_       	=		nn.LayerNorm(__SCREAMING_SNAKE_CASE   )
      elif norm_in_type is None:
        lowerCamelCase_       	=		None
      else:
        raise ValueError(F'''Unsupported norm_in_type: {norm_in_type}.'''   )

      lowerCamelCase_       	=		nn.LayerNorm(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		nn.Linear(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.full(
          [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]			,		-10_000.0   )
      causal_attention_mask.triu_(1   )
      lowerCamelCase_       	=		causal_attention_mask[None, ...]
      self.register_buffer('causal_attention_mask'			,		__SCREAMING_SNAKE_CASE			,		persistent=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		nn.Parameter(torch.zeros(1			,		__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		nn.Parameter(torch.zeros(1			,		__SCREAMING_SNAKE_CASE   )   )






    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def   UpperCamelCase			(			self	:				List[str]   )	->						Dict[str, AttentionProcessor]:
      lowerCamelCase_       	=		{}

      def fn_recursive_add_processors(__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				torch.nn.Module			,		__SCREAMING_SNAKE_CASE	:				Dict[str, AttentionProcessor]   ):
        if hasattr(__SCREAMING_SNAKE_CASE			,		'set_processor'   ):
          lowerCamelCase_       	=		module.processor

        for sub_name, child in module.named_children():
          fn_recursive_add_processors(F'''{name}.{sub_name}'''			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

        return processors

      for name, module in self.named_children():
        fn_recursive_add_processors(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      return processors






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Union[AttentionProcessor, Dict[str, AttentionProcessor]]   )	->						Optional[int]:
      lowerCamelCase_       	=		len(self.attn_processors.keys()   )

      if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) and len(__SCREAMING_SNAKE_CASE   ) != count:
        raise ValueError(
            F'''A dict of processors was passed, but the number of processors {len(__SCREAMING_SNAKE_CASE   )} does not match the'''
            F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''   )

      def fn_recursive_attn_processor(__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				torch.nn.Module			,		__SCREAMING_SNAKE_CASE	:				str   ):
        if hasattr(__SCREAMING_SNAKE_CASE			,		'set_processor'   ):
          if not isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
            module.set_processor(__SCREAMING_SNAKE_CASE   )
          else:
            module.set_processor(processor.pop(F'''{name}.processor'''   )   )

        for sub_name, child in module.named_children():
          fn_recursive_attn_processor(F'''{name}.{sub_name}'''			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      for name, module in self.named_children():
        fn_recursive_attn_processor(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      self.set_attn_processor(AttnProcessor()   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Union[torch.Tensor, float, int]			,		__SCREAMING_SNAKE_CASE	:				torch.FloatTensor			,		__SCREAMING_SNAKE_CASE	:				Optional[torch.FloatTensor] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[torch.BoolTensor] = None			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		)	->						List[Any]:
      lowerCamelCase_       	=		hidden_states.shape[0]

      lowerCamelCase_       	=		timestep
      if not torch.is_tensor(__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		torch.tensor([timesteps]			,		dtype=torch.long			,		device=hidden_states.device   )
      elif torch.is_tensor(__SCREAMING_SNAKE_CASE   ) and len(timesteps.shape   ) == 0:
        lowerCamelCase_       	=		timesteps[None].to(hidden_states.device   )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      lowerCamelCase_       	=		timesteps * torch.ones(__SCREAMING_SNAKE_CASE			,		dtype=timesteps.dtype			,		device=timesteps.device   )

      lowerCamelCase_       	=		self.time_proj(__SCREAMING_SNAKE_CASE   )

      # timesteps does not contain any weights and will always return f32 tensors
      # but time_embedding might be fp16, so we need to cast here.
      lowerCamelCase_       	=		timesteps_projected.to(dtype=self.dtype   )
      lowerCamelCase_       	=		self.time_embedding(__SCREAMING_SNAKE_CASE   )

      if self.embedding_proj_norm is not None:
        lowerCamelCase_       	=		self.embedding_proj_norm(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.embedding_proj(__SCREAMING_SNAKE_CASE   )
      if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
        lowerCamelCase_       	=		self.encoder_hidden_states_proj(__SCREAMING_SNAKE_CASE   )
      elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
        raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'   )

      lowerCamelCase_       	=		self.proj_in(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.positional_embedding.to(hidden_states.dtype   )

      lowerCamelCase_       	=		[]
      lowerCamelCase_       	=		0

      if encoder_hidden_states is not None:
        additional_embeds.append(__SCREAMING_SNAKE_CASE   )
        additional_embeddings_len += encoder_hidden_states.shape[1]

      if len(proj_embeddings.shape   ) == 2:
        lowerCamelCase_       	=		proj_embeddings[:, None, :]

      if len(hidden_states.shape   ) == 2:
        lowerCamelCase_       	=		hidden_states[:, None, :]

      lowerCamelCase_       	=		additional_embeds + [
          proj_embeddings,
          time_embeddings[:, None, :],
          hidden_states,
      ]

      if self.prd_embedding is not None:
        lowerCamelCase_       	=		self.prd_embedding.to(hidden_states.dtype   ).expand(__SCREAMING_SNAKE_CASE			,		-1			,		-1   )
        additional_embeds.append(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.cat(
          __SCREAMING_SNAKE_CASE			,		dim=1			,		)

      # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
      lowerCamelCase_       	=		additional_embeddings_len + proj_embeddings.shape[1] + 1
      if positional_embeddings.shape[1] < hidden_states.shape[1]:
        lowerCamelCase_       	=		F.pad(
            __SCREAMING_SNAKE_CASE			,		(
                0,
                0,
                additional_embeddings_len,
                self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
            )			,		value=0.0			,		)

      lowerCamelCase_       	=		hidden_states + positional_embeddings

      if attention_mask is not None:
        lowerCamelCase_       	=		(1 - attention_mask.to(hidden_states.dtype   )) * -10_000.0
        lowerCamelCase_       	=		F.pad(__SCREAMING_SNAKE_CASE			,		(0, self.additional_embeddings)			,		value=0.0   )
        lowerCamelCase_       	=		(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype   )
        lowerCamelCase_       	=		attention_mask.repeat_interleave(self.config.num_attention_heads			,		dim=0   )

      if self.norm_in is not None:
        lowerCamelCase_       	=		self.norm_in(__SCREAMING_SNAKE_CASE   )

      for block in self.transformer_blocks:
        lowerCamelCase_       	=		block(__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.norm_out(__SCREAMING_SNAKE_CASE   )

      if self.prd_embedding is not None:
        lowerCamelCase_       	=		hidden_states[:, -1]
      else:
        lowerCamelCase_       	=		hidden_states[:, additional_embeddings_len:]

      lowerCamelCase_       	=		self.proj_to_clip_embeddings(__SCREAMING_SNAKE_CASE   )

      if not return_dict:
        return (predicted_image_embedding,)

      return PriorTransformerOutput(predicted_image_embedding=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Optional[int]:
      lowerCamelCase_       	=		(prior_latents * self.clip_std) + self.clip_mean
      return prior_latents




